The transportation of goods from point of origin to a destination plays an important role in the world economy, and often goods must be moved through a complex network of carriers. In the context of global commerce, delivery of an order to a destination from the point of manufacture or packaging to a final destination, the order may traverse various legs comprising (for example) an overland segment (e.g., by truck), a segment across an ocean (e.g., by seagoing vessel), possibly an air segment (e.g., by aircraft), and in some cases even additional segments (e.g., additional overland segments). Some order management systems propose routing for orders, and techniques for such routing may account for shipment using as many legs as may be needed to route from a source location to a destination location. Some systems attempt to account for practical considerations (e.g., timing of delivery) as well as efficiency considerations (e.g., aggregate cost incurred by traversing all legs of an itinerary), yet these legacy techniques still fall short.
Some legacy techniques are able to identify a shortest path from a source to a destination for a particular order (for example a large order), and then find other orders with the same source and destination regions so as to consolidate those orders into one shipment along the identified shortest path. Unfortunately, such legacy techniques fail to consider order consolidation constraints that are inherent in the order data rather than the transportation network itself. For example, legacy techniques may consolidate orders based upon the geography of order locations within the network, without considering whether order pickup time windows or other order pickup-level constraints allow this combination to be feasible. For another example, legacy techniques may consolidate orders based upon capacities defined in the transportation network (e.g., the capacities of the types of trucks available in the network), without considering whether the characteristics of the size or contents of the order or orders satisfy sufficient capacity loading constraints to allow this combination to be feasible.
Legacy techniques are rife with such artificially or falsely simplified constraints. Further legacy techniques that rely on shortest path routing fail to consider refined consolidation constraints derived from pre-consolidation of orders based on order-specific data when considering the consolidation of orders. What is needed is a technique or techniques for deriving refined consolidation constraints in orders before attempting to find minimum cost transportation routes for orders through a transportation network.
None of the aforementioned legacy techniques have the capabilities to perform the herein-disclosed techniques for finding minimum cost transportation routes by using refined consolidation constraints. Therefore, there is a need for an improved approach.